{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import ExtraTreeRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_absolute_error #平方绝对误差\n",
    "from sklearn.metrics import r2_score#R square\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "#显示所有列\n",
    "pd.set_option('display.max_columns', None)\n",
    "#显示所有行\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = pd.read_excel(\"变化轨迹.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>辛烷值</th>\n",
       "      <th>硫含量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.0900</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>88.1100</td>\n",
       "      <td>3.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88.1200</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>88.1400</td>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>88.1540</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>88.1589</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>88.2100</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>88.2400</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>88.2680</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>88.2900</td>\n",
       "      <td>3.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>88.3240</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>88.4400</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>88.4620</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>88.5320</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>88.5500</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>88.6100</td>\n",
       "      <td>3.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>88.6800</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>88.7100</td>\n",
       "      <td>3.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        辛烷值   硫含量\n",
       "0   88.0900  3.20\n",
       "1   88.1100  3.20\n",
       "2   88.1200  3.18\n",
       "3   88.1400  3.15\n",
       "4   88.1540  3.14\n",
       "5   88.1589  3.14\n",
       "6   88.2100  3.12\n",
       "7   88.2400  3.12\n",
       "8   88.2680  3.11\n",
       "9   88.2900  3.14\n",
       "10  88.3240  3.12\n",
       "11  88.4400  3.13\n",
       "12  88.4620  3.12\n",
       "13  88.5320  3.12\n",
       "14  88.5500  3.11\n",
       "15  88.6100  3.11\n",
       "16  88.6800  3.10\n",
       "17  88.7100  3.10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.read_excel(\"主要特征变量.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>主要特征变量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>S-ZORB.CAL_H2.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S-ZORB.FC_2801.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S-ZORB.FT_5101.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S-ZORB.FT_9001.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S-ZORB.LC_5001.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>S-ZORB.LC_5101.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>S-ZORB.PC_2105.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>S-ZORB.PC_5101.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>S-ZORB.PDT_2104.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>S-ZORB.PT_2101.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>S-ZORB.PT_2801.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>S-ZORB.TC_5005.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>S-ZORB.TE_2005.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>S-ZORB.TE_2103.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>S-ZORB.TE_2301.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>S-ZORB.TE_5102.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>S-ZORB.TE_5202.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>S-ZORB.TE_9001.PV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>待生吸附剂性质：S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>待生吸附剂性质：焦炭</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>原料性质：饱和烃</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>原料性质：硫含量</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>原料性质：密度</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>原料性质：烯烃</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>原料性质：辛烷值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>原料性质：溴值</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>再生吸附剂性质：S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>再生吸附剂性质：焦炭</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                主要特征变量\n",
       "0     S-ZORB.CAL_H2.PV\n",
       "1    S-ZORB.FC_2801.PV\n",
       "2    S-ZORB.FT_5101.PV\n",
       "3    S-ZORB.FT_9001.PV\n",
       "4    S-ZORB.LC_5001.PV\n",
       "5    S-ZORB.LC_5101.PV\n",
       "6    S-ZORB.PC_2105.PV\n",
       "7    S-ZORB.PC_5101.PV\n",
       "8   S-ZORB.PDT_2104.PV\n",
       "9    S-ZORB.PT_2101.PV\n",
       "10   S-ZORB.PT_2801.PV\n",
       "11   S-ZORB.TC_5005.PV\n",
       "12   S-ZORB.TE_2005.PV\n",
       "13   S-ZORB.TE_2103.PV\n",
       "14   S-ZORB.TE_2301.PV\n",
       "15   S-ZORB.TE_5102.PV\n",
       "16   S-ZORB.TE_5202.PV\n",
       "17   S-ZORB.TE_9001.PV\n",
       "18           待生吸附剂性质：S\n",
       "19          待生吸附剂性质：焦炭\n",
       "20            原料性质：饱和烃\n",
       "21            原料性质：硫含量\n",
       "22             原料性质：密度\n",
       "23             原料性质：烯烃\n",
       "24            原料性质：辛烷值\n",
       "25             原料性质：溴值\n",
       "26           再生吸附剂性质：S\n",
       "27          再生吸附剂性质：焦炭"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_ = list(f['主要特征变量'].iloc[:18])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['S-ZORB.CAL_H2.PV',\n",
       " 'S-ZORB.FC_2801.PV',\n",
       " 'S-ZORB.FT_5101.PV',\n",
       " 'S-ZORB.FT_9001.PV',\n",
       " 'S-ZORB.LC_5001.PV',\n",
       " 'S-ZORB.LC_5101.PV',\n",
       " 'S-ZORB.PC_2105.PV',\n",
       " 'S-ZORB.PC_5101.PV',\n",
       " 'S-ZORB.PDT_2104.PV',\n",
       " 'S-ZORB.PT_2101.PV',\n",
       " 'S-ZORB.PT_2801.PV',\n",
       " 'S-ZORB.TC_5005.PV',\n",
       " 'S-ZORB.TE_2005.PV',\n",
       " 'S-ZORB.TE_2103.PV',\n",
       " 'S-ZORB.TE_2301.PV',\n",
       " 'S-ZORB.TE_5102.PV',\n",
       " 'S-ZORB.TE_5202.PV',\n",
       " 'S-ZORB.TE_9001.PV']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ax1 = fig.add_subplot(111)\n",
    "# ax1.plot(x, y1)\n",
    "# ax1.set_ylabel('Y values for exp(-x)')\n",
    "# ax1.set_title(\"Double Y axis\")\n",
    "\n",
    "# ax2 = ax1.twinx()  # this is the important function\n",
    "# ax2.plot(x, y2, 'r')\n",
    "# ax2.set_xlim([0, np.e])\n",
    "# ax2.set_ylabel('Y values for ln(x)')\n",
    "# ax2.set_xlabel('Same X for both exp(-x) and ln(x)')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(15, 8))\n",
    "ax1 = fig.add_subplot(111)\n",
    "plt.title(\"产品辛烷值-硫含量变化轨迹图\")\n",
    "ax1.plot(x_, val[\"辛烷值\"], label=\"辛烷值\")\n",
    "plt.xticks(rotation=90)\n",
    "ax1.set_ylabel(\"辛烷值\")\n",
    "plt.legend()\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax2.plot(x_, val[\"硫含量\"], 'r', label=\"硫含量\")\n",
    "ax2.set_ylabel(\"硫含量\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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